4,336 research outputs found
Effects of depolarizing quantum channels on BB84 and SARG04 quantum cryptography protocols
We report experimental studies on the effect of the depolarizing quantum
channel on weak-pulse BB84 and SARG04 quantum cryptography. The experimental
results show that, in real world conditions in which channel depolarization
cannot be ignored, BB84 should perform better than SARG04.Comment: 4 pages, 4 figure
Terrace solutions for non-Lipschitz multistable nonlinearities
Traveling wave solutions of reaction-diffusion equations are well-studied for
Lipschitz continuous monostable and bistable reaction functions. These special
solutions play a key role in mathematical biology and in particular in the
study of ecological invasions. However, if there are more than two stable
steady states, the invasion phenomenon may become more intricate and involve
intermediate steps, which leads one to consider not a single but a collection
of traveling waves with ordered speeds. In this paper we show that, if the
reaction function is discontinuous at the stable steady states, then such a
collection of traveling waves exists and even provides a special solution which
we call a terrace solution. More precisely, we will address both the existence
and uniqueness of the terrace solution
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Comparison of the Plasma Metabolome Profiles Between the Internal Thoracic Artery and Ascending Aorta in Patients Undergoing Coronary Artery Bypass Graft Surgery Using Gas Chromatography Time-of-Flight Mass Spectrometry.
BackgroundThe left internal thoracic artery (LITA) has been used as the first conduit of choice in coronary artery bypass grafting (CABG) because of excellent long-term patency and outcomes. However, no studies have examined substances other than nitric oxide that could be beneficial for the bypass conduit, native coronary artery or ischemic myocardium. This study was conducted to evaluate differences in metabolic profiles between the LITA and ascending aorta using gas chromatography-time of flight-mass spectrometry (GC-TOF-MS).MethodsTwenty patients who underwent CABG using the LITA were prospectively enrolled. Plasma samples were collected simultaneously from the LITA and ascending aorta. GC-TOF-MS based untargeted metabolomic analyses were performed and a 2-step volcano plot analysis was used to identify distinguishable markers from two plasma metabolome profiles. Semi-quantitative and quantitative analyses were performed using GC-TOF-MS and enzyme-linked immunosorbent assay, respectively, after selecting target metabolites based on the metabolite set enrichment analysis.ResultsInitial volcano plot analysis demonstrated 5 possible markers among 851 peaks detected. The final analysis demonstrated that the L-cysteine peak was significantly higher in the LITA than in the ascending aorta (fold change = 1.86). The concentrations of intermediate metabolites such as L-cysteine, L-methionine and L-cystine in the 'cysteine and methionine metabolism pathway' were significantly higher in the LITA than in the ascending aorta (2.0-, 1.4- and 1.2-fold, respectively). Quantitative analysis showed that the concentration of hydrogen sulfide (H₂S) was significantly higher in the LITA.ConclusionThe plasma metabolome profiles of the LITA and ascending aorta were different, particularly higher plasma concentrations of L-cysteine and H₂S in the LITA
CXR-LLAVA: a multimodal large language model for interpreting chest X-ray images
Purpose: This study aimed to develop an open-source multimodal large language
model (CXR-LLAVA) for interpreting chest X-ray images (CXRs), leveraging recent
advances in large language models (LLMs) to potentially replicate the image
interpretation skills of human radiologists Materials and Methods: For
training, we collected 592,580 publicly available CXRs, of which 374,881 had
labels for certain radiographic abnormalities (Dataset 1) and 217,699 provided
free-text radiology reports (Dataset 2). After pre-training a vision
transformer with Dataset 1, we integrated it with an LLM influenced by the
LLAVA network. Then, the model was fine-tuned, primarily using Dataset 2. The
model's diagnostic performance for major pathological findings was evaluated,
along with the acceptability of radiologic reports by human radiologists, to
gauge its potential for autonomous reporting. Results: The model demonstrated
impressive performance in test sets, achieving an average F1 score of 0.81 for
six major pathological findings in the MIMIC internal test set and 0.62 for
seven major pathological findings in the external test set. The model's F1
scores surpassed those of GPT-4-vision and Gemini-Pro-Vision in both test sets.
In human radiologist evaluations of the external test set, the model achieved a
72.7% success rate in autonomous reporting, slightly below the 84.0% rate of
ground truth reports. Conclusion: This study highlights the significant
potential of multimodal LLMs for CXR interpretation, while also acknowledging
the performance limitations. Despite these challenges, we believe that making
our model open-source will catalyze further research, expanding its
effectiveness and applicability in various clinical contexts. CXR-LLAVA is
available at https://github.com/ECOFRI/CXR_LLAVA
Strangeness-driven Exploration in Multi-Agent Reinforcement Learning
Efficient exploration strategy is one of essential issues in cooperative
multi-agent reinforcement learning (MARL) algorithms requiring complex
coordination. In this study, we introduce a new exploration method with the
strangeness that can be easily incorporated into any centralized training and
decentralized execution (CTDE)-based MARL algorithms. The strangeness refers to
the degree of unfamiliarity of the observations that an agent visits. In order
to give the observation strangeness a global perspective, it is also augmented
with the the degree of unfamiliarity of the visited entire state. The
exploration bonus is obtained from the strangeness and the proposed exploration
method is not much affected by stochastic transitions commonly observed in MARL
tasks. To prevent a high exploration bonus from making the MARL training
insensitive to extrinsic rewards, we also propose a separate action-value
function trained by both extrinsic reward and exploration bonus, on which a
behavioral policy to generate transitions is designed based. It makes the
CTDE-based MARL algorithms more stable when they are used with an exploration
method. Through a comparative evaluation in didactic examples and the StarCraft
Multi-Agent Challenge, we show that the proposed exploration method achieves
significant performance improvement in the CTDE-based MARL algorithms.Comment: 9 pages, 7 figure
Future development strategies for KODISA journals: overview of 2016 and strategic plans for the future
Purpose – With the rise of the fourth industrial revolution, it has converged with the existing industrial revolution to give shape to increased accessibility of knowledge and information. As a result, it has become easier for scholars to actively pursue and compile research in various fields. This current study aims to focus and assess the current standing of KODISA: the Journal of Distribution Science (JDS), International Journal of Industrial Distribution & Business (IJIDB), the East Asian Journal of Business Management (EAJBM), the Journal of Asian Finance, Economics and Business (JAFEB) in a rapidly evolving era. Novel strategies for creating the future vision of KODISA 2020 will also be examined. Research design, data, and methodology – The current research will analyze published journals of KODISA in order to offer a vision for the KODISA 2020 future. In part 1, this paper will observe the current address of the KODISA journal and its overview of past achievements. Next, part 2 will discuss the activities that will be needed for journals of KODISA, JDS, IJIDB, EAJBM, JAFEB to branch out internationally and significant journals will be statistically analyzed in part 3. The last part 4 will offer strategies for the continued growth of KODISA and visions for KODISA 2020. Results – Among the KODISA publications, IJIDB was second, JDS was 23rd (in economic publications of 54 journals), and EAJBM was 22nd (out of 79 publications in management field journals). This shows the high quality of the KODISA publication journals. According to 2016 publication analysis, JDS, IJIDB, etc. each had 157 publications, 15 publications, 16 publications, and 28 publications. In the case of JDS, it showed an increase of 14% compared to last year. Additionally, JAFEB showed a significant increase of 68%. This shows that compared to other journals, it had a higher rate of paper submission. IJIDB and EAJBM did not show any significant increases. In JDS, it showed many studies related to the distribution, management of distribution, and consumer behavior. In order to increase the status of the KODISA journal to a SCI status, many more international conferences will open to increase its international recognition levels. Second, the systematic functions of the journal will be developed further to increase its stability. Third, future graduate schools will open to foster future potential leaders in this field and build a platform for innovators and leaders. Conclusions – In KODISA, JDS was first published in 1999, and has been registered in SCOPUS February 2017. Other sister publications within the KODISA are preparing for SCOPUS registration as well. KODISA journals will prepare to be an innovative journal for 2020 and the future beyond
Development of a classification model for Cynanchum wilfordii and Cynanchum auriculatum using convolutional neural network and local interpretable model-agnostic explanation technology
Cynanchum wilfordii is a perennial tuberous root in the Asclepiadaceae family that has long been used medicinally. Although C. wilfordii is distinct in origin and content from Cynancum auriculatum, a genus of the same species, it is difficult for the public to recognize because the ripe fruit and root are remarkably similar. In this study, images were collected to categorize C. wilfordii and C. auriculatum, which were then processed and input into a deep-learning classification model to corroborate the results. By obtaining 200 photographs of each of the two cross sections of each medicinal material, approximately 800 images were employed, and approximately 3200 images were used to construct a deep-learning classification model via image augmentation. For the classification, the structures of Inception-ResNet and VGGnet-19 among convolutional neural network (CNN) models were used, with Inception-ResNet outperforming VGGnet-19 in terms of performance and learning speed. The validation set confirmed a strong classification performance of approximately 0.862. Furthermore, explanatory properties were added to the deep-learning model using local interpretable model-agnostic explanation (LIME), and the suitability of the LIME domain was assessed using cross-validation in both situations. Thus, artificial intelligence may be used as an auxiliary metric in the sensory evaluation of medicinal materials in future, owing to its explanatory ability
Cases of ethical violation in research publications: through editorial decision making process
Purpose – To improve and strengthen existing publication and research ethics, KODISA has identified and presented various cases which have violated publication and research ethics and principles in recent years. The editorial office of KODISA has been providing and continues to provide advice and feedback on publication ethics to researchers during peer review and editorial decision making process. Providing advice and feedback on publication ethics will ensure researchers to have an opportunity to correct their mistakes or make appropriate decisions and avoid any violations in research ethics. The purpose of this paper is to identify different cases of ethical violation in research and inform and educate researchers to avoid any violations in publication and research ethics. Furthermore, this article will demonstrate how KODISA journals identify and penalize ethical violations and strengthens its publication ethics and practices. Research design, data and methodology – This paper examines different types of ethical violation in publication and research ethics. The paper identifies and analyzes all ethical violations in research and combines them into five general categories. Those five general types of ethical violations are thoroughly examined and discussed. Results – Ethical violations of research occur in various forms at regular intervals; in other words, unethical researchers tend to commit different types of ethical violations repeatedly at same time. The five categories of ethical violation in research are as follows: (1) Arbitrary changes or additions in author(s) happen frequently in thesis/dissertation related publications. (2) Self plagiarism, submitting same work or mixture of previous works with or without using proper citations, also occurs frequently, but the most common type of plagiarism is changing the statistical results and using them to present as the results of the empirical analysis; (3) Translation plagiarism, another ethical violation in publication, is difficult to detect but occurs frequently; (4) Fabrication of data or statistical analysis also occurs frequently. KODISA requires authors to submit the results of the empirical analysis of the paper (the output of the statistical program) to prevent this type of ethical violation; (5) Mashup or aggregator plagiarism, submitting a mix of several different works with or without proper citations without alterations, is very difficult to detect, and KODISA journals consider this type of plagiarism as the worst ethical violation. Conclusions – There are some individual cases of ethical violation in research and publication that could not be included in the five categories presented throughout the paper. KODISA and its editorial office should continue to develop, revise, and strengthen their publication ethics, to learn and share different ways to detect any ethical violations in research and publication, to train and educate its editorial members and researchers, and to analyze and share different cases of ethical violations with the scholarly community
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